| ## @package hypothesis_test_util |
| # Module caffe2.python.hypothesis_test_util |
| """ |
| The Hypothesis library uses *property-based testing* to check |
| invariants about the code under test under a variety of random inputs. |
| |
| The key idea here is to express properties of the code under test |
| (e.g. that it passes a gradient check, that it implements a reference |
| function, etc), and then generate random instances and verify they |
| satisfy these properties. |
| |
| The main functions of interest are exposed on `HypothesisTestCase`. |
| You can usually just add a short function in this to generate an |
| arbitrary number of test cases for your operator. |
| |
| The key functions are: |
| |
| - `assertDeviceChecks(devices, op, inputs, outputs)`. This asserts that the |
| operator computes the same outputs, regardless of which device it is executed |
| on. |
| - `assertGradientChecks(device, op, inputs, output_, |
| outputs_with_grads)`. This implements a standard numerical gradient checker |
| for the operator in question. |
| - `assertReferenceChecks(device, op, inputs, reference)`. This runs the |
| reference function (effectively calling `reference(*inputs)`, and comparing |
| that to the output of output. |
| |
| `hypothesis_test_util.py` exposes some useful pre-built samplers. |
| |
| - `hu.gcs` - a gradient checker device (`gc`) and device checker devices (`dc`) |
| |
| - `hu.gcs_cpu_only` - a CPU-only gradient checker device (`gc`) and |
| device checker devices (`dc`). Used for when your operator is only |
| implemented on the CPU. |
| """ |
| |
| |
| |
| |
| |
| from caffe2.proto import caffe2_pb2 |
| from caffe2.python import ( |
| workspace, device_checker, gradient_checker, test_util, core) |
| import contextlib |
| import copy |
| import functools |
| import hypothesis |
| import hypothesis.extra.numpy |
| import hypothesis.strategies as st |
| import logging |
| import numpy as np |
| import os |
| import six |
| import struct |
| |
| |
| def is_sandcastle(): |
| return os.getenv('SANDCASTLE') == '1' or os.getenv('TW_JOB_USER') == 'sandcastle' |
| |
| |
| def is_travis(): |
| return 'TRAVIS' in os.environ |
| |
| |
| def to_float32(x): |
| return struct.unpack("f", struct.pack("f", float(x)))[0] |
| |
| |
| # "min_satisfying_examples" setting has been deprecated in hypothesis |
| # 3.56.0 and removed in hypothesis 4.x |
| def settings(*args, **kwargs): |
| if 'min_satisfying_examples' in kwargs and hypothesis.version.__version_info__ >= (3, 56, 0): |
| kwargs.pop('min_satisfying_examples') |
| |
| if 'deadline' in kwargs and hypothesis.version.__version_info__ < (4, 44, 0): |
| kwargs.pop('deadline') |
| |
| if 'timeout' in kwargs and hypothesis.version.__version_info__ >= (4, 44, 0): |
| if 'deadline' not in kwargs: |
| kwargs['deadline'] = kwargs['timeout'] * 1e3 |
| kwargs.pop('timeout') |
| |
| return hypothesis.settings(*args, **kwargs) |
| |
| # This wrapper wraps around `st.floats` and |
| # sets width parameters to 32 if version is newer than 3.67.0 |
| def floats(*args, **kwargs): |
| |
| width_supported = hypothesis.version.__version_info__ >= (3, 67, 0) |
| if 'width' in kwargs and not width_supported: |
| kwargs.pop('width') |
| |
| if 'width' not in kwargs and width_supported: |
| kwargs['width'] = 32 |
| if kwargs.get('min_value', None) is not None: |
| kwargs['min_value'] = to_float32(kwargs['min_value']) |
| if kwargs.get('max_value', None) is not None: |
| kwargs['max_value'] = to_float32(kwargs['max_value']) |
| |
| return st.floats(*args, **kwargs) |
| |
| |
| hypothesis.settings.register_profile( |
| "sandcastle", |
| settings( |
| derandomize=True, |
| suppress_health_check=[hypothesis.HealthCheck.too_slow], |
| database=None, |
| max_examples=50, |
| min_satisfying_examples=1, |
| verbosity=hypothesis.Verbosity.verbose, |
| deadline=10000)) |
| hypothesis.settings.register_profile( |
| "dev", |
| settings( |
| suppress_health_check=[hypothesis.HealthCheck.too_slow], |
| database=None, |
| max_examples=10, |
| min_satisfying_examples=1, |
| verbosity=hypothesis.Verbosity.verbose, |
| deadline=10000)) |
| hypothesis.settings.register_profile( |
| "debug", |
| settings( |
| suppress_health_check=[hypothesis.HealthCheck.too_slow], |
| database=None, |
| max_examples=1000, |
| min_satisfying_examples=1, |
| verbosity=hypothesis.Verbosity.verbose, |
| deadline=50000)) |
| |
| hypothesis.settings.load_profile( |
| 'sandcastle' if is_sandcastle() else os.getenv('CAFFE2_HYPOTHESIS_PROFILE', |
| 'dev') |
| ) |
| |
| |
| def dims(min_value=1, max_value=5): |
| return st.integers(min_value=min_value, max_value=max_value) |
| |
| |
| def elements_of_type(dtype=np.float32, filter_=None): |
| elems = None |
| if dtype is np.float16: |
| elems = floats(min_value=-1.0, max_value=1.0, width=16) |
| elif dtype is np.float32: |
| elems = floats(min_value=-1.0, max_value=1.0, width=32) |
| elif dtype is np.float64: |
| elems = floats(min_value=-1.0, max_value=1.0, width=64) |
| elif dtype is np.int32: |
| elems = st.integers(min_value=0, max_value=2 ** 31 - 1) |
| elif dtype is np.int64: |
| elems = st.integers(min_value=0, max_value=2 ** 63 - 1) |
| elif dtype is np.bool: |
| elems = st.booleans() |
| else: |
| raise ValueError("Unexpected dtype without elements provided") |
| return elems if filter_ is None else elems.filter(filter_) |
| |
| |
| def arrays(dims, dtype=np.float32, elements=None, unique=False): |
| if elements is None: |
| elements = elements_of_type(dtype) |
| return hypothesis.extra.numpy.arrays( |
| dtype, |
| dims, |
| elements=elements, |
| unique=unique, |
| ) |
| |
| |
| def tensor(min_dim=1, |
| max_dim=4, |
| dtype=np.float32, |
| elements=None, |
| unique=False, |
| **kwargs): |
| dims_ = st.lists(dims(**kwargs), min_size=min_dim, max_size=max_dim) |
| return dims_.flatmap( |
| lambda dims: arrays(dims, dtype, elements, unique=unique)) |
| |
| |
| def tensor1d(min_len=1, max_len=64, dtype=np.float32, elements=None): |
| return tensor(1, 1, dtype, elements, min_value=min_len, max_value=max_len) |
| |
| |
| def segment_ids(size, is_sorted): |
| if size == 0: |
| return st.just(np.empty(shape=[0], dtype=np.int32)) |
| if is_sorted: |
| return arrays( |
| [size], |
| dtype=np.int32, |
| elements=st.booleans()).map( |
| lambda x: np.cumsum(x, dtype=np.int32) - x[0]) |
| else: |
| return arrays( |
| [size], |
| dtype=np.int32, |
| elements=st.integers(min_value=0, max_value=2 * size)) |
| |
| |
| def lengths(size, min_segments=None, max_segments=None, **kwargs): |
| # First generate number of boarders between segments |
| # Then create boarder values and add 0 and size |
| # By sorting and computing diff we convert them to lengths of |
| # possible 0 value |
| if min_segments is None: |
| min_segments = 0 |
| if max_segments is None: |
| max_segments = size |
| assert min_segments >= 0 |
| assert min_segments <= max_segments |
| if size == 0 and max_segments == 0: |
| return st.just(np.empty(shape=[0], dtype=np.int32)) |
| assert max_segments > 0, "size is not 0, need at least one segment" |
| return st.integers( |
| min_value=max(min_segments - 1, 0), max_value=max_segments - 1 |
| ).flatmap( |
| lambda num_borders: |
| hypothesis.extra.numpy.arrays( |
| np.int32, num_borders, elements=st.integers( |
| min_value=0, max_value=size |
| ) |
| ) |
| ).map( |
| lambda x: np.append(x, np.array([0, size], dtype=np.int32)) |
| ).map(sorted).map(np.diff) |
| |
| |
| def segmented_tensor( |
| min_dim=1, |
| max_dim=4, |
| dtype=np.float32, |
| is_sorted=True, |
| elements=None, |
| segment_generator=segment_ids, |
| allow_empty=False, |
| **kwargs |
| ): |
| gen_empty = st.booleans() if allow_empty else st.just(False) |
| data_dims_ = st.lists(dims(**kwargs), min_size=min_dim, max_size=max_dim) |
| data_dims_ = st.tuples( |
| gen_empty, data_dims_ |
| ).map(lambda pair: ([0] if pair[0] else []) + pair[1]) |
| return data_dims_.flatmap(lambda data_dims: st.tuples( |
| arrays(data_dims, dtype, elements), |
| segment_generator(data_dims[0], is_sorted=is_sorted), |
| )) |
| |
| |
| def lengths_tensor(min_segments=None, max_segments=None, *args, **kwargs): |
| gen = functools.partial( |
| lengths, min_segments=min_segments, max_segments=max_segments) |
| return segmented_tensor(*args, segment_generator=gen, **kwargs) |
| |
| |
| def sparse_segmented_tensor(min_dim=1, max_dim=4, dtype=np.float32, |
| is_sorted=True, elements=None, allow_empty=False, |
| segment_generator=segment_ids, itype=np.int64, |
| **kwargs): |
| gen_empty = st.booleans() if allow_empty else st.just(False) |
| data_dims_ = st.lists(dims(**kwargs), min_size=min_dim, max_size=max_dim) |
| all_dims_ = st.tuples(gen_empty, data_dims_).flatmap( |
| lambda pair: st.tuples( |
| st.just(pair[1]), |
| (st.integers(min_value=1, max_value=pair[1][0]) if not pair[0] |
| else st.just(0)), |
| )) |
| return all_dims_.flatmap(lambda dims: st.tuples( |
| arrays(dims[0], dtype, elements), |
| arrays(dims[1], dtype=itype, elements=st.integers( |
| min_value=0, max_value=dims[0][0] - 1)), |
| segment_generator(dims[1], is_sorted=is_sorted), |
| )) |
| |
| |
| def sparse_lengths_tensor(**kwargs): |
| return sparse_segmented_tensor(segment_generator=lengths, **kwargs) |
| |
| |
| def tensors(n, min_dim=1, max_dim=4, dtype=np.float32, elements=None, **kwargs): |
| dims_ = st.lists(dims(**kwargs), min_size=min_dim, max_size=max_dim) |
| return dims_.flatmap( |
| lambda dims: st.lists( |
| arrays(dims, dtype, elements), |
| min_size=n, |
| max_size=n)) |
| |
| |
| def tensors1d(n, min_len=1, max_len=64, dtype=np.float32, elements=None): |
| return tensors( |
| n, 1, 1, dtype, elements, min_value=min_len, max_value=max_len |
| ) |
| |
| |
| cpu_do = caffe2_pb2.DeviceOption() |
| cuda_do = caffe2_pb2.DeviceOption(device_type=caffe2_pb2.CUDA) |
| hip_do = caffe2_pb2.DeviceOption(device_type=caffe2_pb2.HIP) |
| gpu_do = caffe2_pb2.DeviceOption(device_type=workspace.GpuDeviceType) # CUDA or ROCm |
| _cuda_do_list = ([cuda_do] if workspace.has_cuda_support else []) |
| _hip_do_list = ([hip_do] if workspace.has_hip_support else []) |
| _gpu_do_list = ([gpu_do] if workspace.has_gpu_support else []) |
| # (bddppq) Do not rely on this no_hip option! It's just used to |
| # temporarily skip some flaky tests on ROCM before it's getting more mature. |
| _device_options_no_hip = [cpu_do] + _cuda_do_list |
| device_options = _device_options_no_hip + _hip_do_list |
| |
| # Include device option for each GPU |
| expanded_device_options = [cpu_do] + [ |
| caffe2_pb2.DeviceOption(device_type=workspace.GpuDeviceType, device_id=i) |
| for i in range(workspace.NumGpuDevices())] |
| |
| |
| def device_checker_device_options(): |
| return st.just(device_options) |
| |
| |
| def gradient_checker_device_option(): |
| return st.sampled_from(device_options) |
| |
| |
| gcs = dict( |
| gc=gradient_checker_device_option(), |
| dc=device_checker_device_options() |
| ) |
| |
| gcs_cpu_only = dict(gc=st.sampled_from([cpu_do]), dc=st.just([cpu_do])) |
| gcs_cuda_only = dict(gc=st.sampled_from(_cuda_do_list), dc=st.just(_cuda_do_list)) |
| gcs_gpu_only = dict(gc=st.sampled_from(_gpu_do_list), dc=st.just(_gpu_do_list)) # CUDA or ROCm |
| gcs_no_hip = dict(gc=st.sampled_from(_device_options_no_hip), dc=st.just(_device_options_no_hip)) |
| |
| |
| @contextlib.contextmanager |
| def temp_workspace(name=b"temp_ws"): |
| old_ws_name = workspace.CurrentWorkspace() |
| workspace.SwitchWorkspace(name, True) |
| yield |
| workspace.ResetWorkspace() |
| workspace.SwitchWorkspace(old_ws_name) |
| |
| |
| def runOpBenchmark( |
| device_option, |
| op, |
| inputs, |
| input_device_options=None, |
| iterations=10, |
| ): |
| op = copy.deepcopy(op) |
| op.device_option.CopyFrom(device_option) |
| net = caffe2_pb2.NetDef() |
| net.op.extend([op]) |
| net.name = op.name if op.name else "test" |
| |
| with temp_workspace(): |
| _input_device_options = input_device_options or \ |
| core.InferOpBlobDevicesAsDict(op)[0] |
| for (n, b) in zip(op.input, inputs): |
| workspace.FeedBlob( |
| n, |
| b, |
| device_option=_input_device_options.get(n, device_option) |
| ) |
| workspace.CreateNet(net) |
| ret = workspace.BenchmarkNet(net.name, 1, iterations, True) |
| return ret |
| |
| |
| def runOpOnInput( |
| device_option, |
| op, |
| inputs, |
| input_device_options=None, |
| ): |
| op = copy.deepcopy(op) |
| op.device_option.CopyFrom(device_option) |
| |
| with temp_workspace(): |
| if (len(op.input) > len(inputs)): |
| raise ValueError( |
| 'must supply an input for each input on the op: %s vs %s' % |
| (op.input, inputs)) |
| _input_device_options = input_device_options or \ |
| core.InferOpBlobDevicesAsDict(op)[0] |
| for (n, b) in zip(op.input, inputs): |
| workspace.FeedBlob( |
| n, |
| b, |
| device_option=_input_device_options.get(n, device_option) |
| ) |
| workspace.RunOperatorOnce(op) |
| outputs_to_check = list(range(len(op.output))) |
| outs = [] |
| for output_index in outputs_to_check: |
| output_blob_name = op.output[output_index] |
| output = workspace.FetchBlob(output_blob_name) |
| outs.append(output) |
| return outs |
| |
| |
| class HypothesisTestCase(test_util.TestCase): |
| """ |
| A unittest.TestCase subclass with some helper functions for |
| utilizing the `hypothesis` (hypothesis.readthedocs.io) library. |
| """ |
| |
| def assertDeviceChecks( |
| self, |
| device_options, |
| op, |
| inputs, |
| outputs_to_check, |
| input_device_options=None, |
| threshold=0.01 |
| ): |
| """ |
| Asserts that the operator computes the same outputs, regardless of |
| which device it is executed on. |
| |
| Useful for checking the consistency of GPU and CPU |
| implementations of operators. |
| |
| Usage example: |
| |
| @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs) |
| def test_sum(self, inputs, in_place, gc, dc): |
| op = core.CreateOperator("Sum", ["X1", "X2"], |
| ["Y" if not in_place else "X1"]) |
| X1, X2 = inputs |
| self.assertDeviceChecks(dc, op, [X1, X2], [0]) |
| """ |
| dc = device_checker.DeviceChecker( |
| threshold, |
| device_options=device_options |
| ) |
| self.assertTrue( |
| dc.CheckSimple(op, inputs, outputs_to_check, input_device_options) |
| ) |
| |
| def assertGradientChecks( |
| self, |
| device_option, |
| op, |
| inputs, |
| outputs_to_check, |
| outputs_with_grads, |
| grad_ops=None, |
| threshold=0.005, |
| stepsize=0.05, |
| input_device_options=None, |
| ensure_outputs_are_inferred=False, |
| ): |
| """ |
| Implements a standard numerical gradient checker for the operator |
| in question. |
| |
| Useful for checking the consistency of the forward and |
| backward implementations of operators. |
| |
| Usage example: |
| |
| @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs) |
| def test_sum(self, inputs, in_place, gc, dc): |
| op = core.CreateOperator("Sum", ["X1", "X2"], |
| ["Y" if not in_place else "X1"]) |
| X1, X2 = inputs |
| self.assertGradientChecks(gc, op, [X1, X2], 0, [0]) |
| """ |
| gc = gradient_checker.GradientChecker( |
| stepsize=stepsize, |
| threshold=threshold, |
| device_option=device_option, |
| workspace_name=str(device_option), |
| input_device_options=input_device_options, |
| ) |
| res, grad, grad_estimated = gc.CheckSimple( |
| op, inputs, outputs_to_check, outputs_with_grads, |
| grad_ops=grad_ops, |
| input_device_options=input_device_options, |
| ensure_outputs_are_inferred=ensure_outputs_are_inferred, |
| ) |
| self.assertEqual(grad.shape, grad_estimated.shape) |
| self.assertTrue( |
| res, |
| "Gradient check failed for input " + str(op.input[outputs_to_check]) |
| ) |
| |
| def _assertGradReferenceChecks( |
| self, |
| op, |
| inputs, |
| ref_outputs, |
| output_to_grad, |
| grad_reference, |
| threshold=1e-4, |
| ): |
| grad_blob_name = output_to_grad + '_grad' |
| grad_ops, grad_map = core.GradientRegistry.GetBackwardPass( |
| [op], {output_to_grad: grad_blob_name}) |
| output_grad = workspace.FetchBlob(output_to_grad) |
| grad_ref_outputs = grad_reference(output_grad, ref_outputs, inputs) |
| workspace.FeedBlob(grad_blob_name, workspace.FetchBlob(output_to_grad)) |
| workspace.RunOperatorsOnce(grad_ops) |
| |
| self.assertEqual(len(grad_ref_outputs), len(inputs)) |
| for (n, ref) in zip(op.input, grad_ref_outputs): |
| grad_names = grad_map.get(n) |
| if not grad_names: |
| # no grad for this input |
| self.assertIsNone(ref) |
| else: |
| if isinstance(grad_names, core.BlobReference): |
| # dense gradient |
| ref_vals = ref |
| ref_indices = None |
| val_name = grad_names |
| else: |
| # sparse gradient |
| ref_vals, ref_indices = ref |
| val_name = grad_names.values |
| vals = workspace.FetchBlob(str(val_name)) |
| np.testing.assert_allclose( |
| vals, |
| ref_vals, |
| atol=threshold, |
| rtol=threshold, |
| err_msg='Gradient {0} (x) is not matching the reference (y)' |
| .format(val_name), |
| ) |
| if ref_indices is not None: |
| indices = workspace.FetchBlob(str(grad_names.indices)) |
| np.testing.assert_allclose(indices, ref_indices, |
| atol=1e-4, rtol=1e-4) |
| |
| def _assertInferTensorChecks(self, name, shapes, types, output, |
| ensure_output_is_inferred=False): |
| self.assertTrue( |
| not ensure_output_is_inferred or (name in shapes), |
| 'Shape for {0} was not inferred'.format(name)) |
| |
| if name not in shapes: |
| # No inferred shape or type available |
| return |
| output = workspace.FetchBlob(name) |
| if type(output) is np.ndarray: |
| if output.dtype == np.dtype('float64'): |
| correct_type = caffe2_pb2.TensorProto.DOUBLE |
| elif output.dtype == np.dtype('float32'): |
| correct_type = caffe2_pb2.TensorProto.FLOAT |
| elif output.dtype == np.dtype('int32'): |
| correct_type = caffe2_pb2.TensorProto.INT32 |
| elif output.dtype == np.dtype('int64'): |
| correct_type = caffe2_pb2.TensorProto.INT64 |
| else: |
| correct_type = "unknown {}".format(np.dtype) |
| else: |
| correct_type = str(type(output)) |
| try: |
| np.testing.assert_array_equal( |
| np.array(shapes[name]).astype(np.int32), |
| np.array(output.shape).astype(np.int32), |
| err_msg='Shape {} mismatch: {} vs. {}'.format( |
| name, |
| shapes[name], |
| output.shape)) |
| # BUG: Workspace blob type not being set correctly T16121392 |
| if correct_type != caffe2_pb2.TensorProto.INT32: |
| return |
| np.testing.assert_equal( |
| types[name], |
| correct_type, |
| err_msg='Type {} mismatch: {} vs. {}'.format( |
| name, types[name], correct_type, |
| ) |
| ) |
| except AssertionError as e: |
| # Temporarily catch these assertion errors when validating |
| # inferred shape and type info |
| logging.warning(str(e)) |
| if os.getenv('CAFFE2_ASSERT_SHAPEINFERENCE') == '1' or ensure_output_is_inferred: |
| raise e |
| |
| def assertReferenceChecks( |
| self, |
| device_option, |
| op, |
| inputs, |
| reference, |
| input_device_options=None, |
| threshold=1e-4, |
| output_to_grad=None, |
| grad_reference=None, |
| atol=None, |
| outputs_to_check=None, |
| ensure_outputs_are_inferred=False, |
| ): |
| """ |
| This runs the reference Python function implementation |
| (effectively calling `reference(*inputs)`, and compares that |
| to the output of output, with an absolute/relative tolerance |
| given by the `threshold` parameter. |
| |
| Useful for checking the implementation matches the Python |
| (typically NumPy) implementation of the same functionality. |
| |
| Usage example: |
| |
| @given(X=hu.tensor(), inplace=st.booleans(), **hu.gcs) |
| def test_softsign(self, X, inplace, gc, dc): |
| op = core.CreateOperator( |
| "Softsign", ["X"], ["X" if inplace else "Y"]) |
| |
| def softsign(X): |
| return (X / (1 + np.abs(X)),) |
| |
| self.assertReferenceChecks(gc, op, [X], softsign) |
| """ |
| op = copy.deepcopy(op) |
| op.device_option.CopyFrom(device_option) |
| |
| with temp_workspace(): |
| if (len(op.input) > len(inputs)): |
| raise ValueError( |
| 'must supply an input for each input on the op: %s vs %s' % |
| (op.input, inputs)) |
| _input_device_options = input_device_options or \ |
| core.InferOpBlobDevicesAsDict(op)[0] |
| for (n, b) in zip(op.input, inputs): |
| workspace.FeedBlob( |
| n, |
| b, |
| device_option=_input_device_options.get(n, device_option) |
| ) |
| net = core.Net("opnet") |
| net.Proto().op.extend([op]) |
| test_shape_inference = False |
| try: |
| (shapes, types) = workspace.InferShapesAndTypes([net]) |
| test_shape_inference = True |
| except RuntimeError as e: |
| # Temporarily catch runtime errors when inferring shape |
| # and type info |
| logging.warning(str(e)) |
| if os.getenv('CAFFE2_ASSERT_SHAPEINFERENCE') == '1' or ensure_outputs_are_inferred: |
| raise e |
| workspace.RunNetOnce(net) |
| reference_outputs = reference(*inputs) |
| if not (isinstance(reference_outputs, tuple) or |
| isinstance(reference_outputs, list)): |
| raise RuntimeError( |
| "You are providing a wrong reference implementation. A " |
| "proper one should return a tuple/list of numpy arrays.") |
| if not outputs_to_check: |
| self.assertEqual(len(reference_outputs), len(op.output)) |
| outputs_to_check = list(range(len(op.output))) |
| outs = [] |
| for (output_index, ref) in zip(outputs_to_check, reference_outputs): |
| output_blob_name = op.output[output_index] |
| output = workspace.FetchBlob(output_blob_name) |
| if output.dtype.kind in ('S', 'O'): |
| np.testing.assert_array_equal(output, ref) |
| else: |
| if atol is None: |
| atol = threshold |
| np.testing.assert_allclose( |
| output, ref, atol=atol, rtol=threshold, |
| err_msg=( |
| 'Output {0} is not matching the reference'.format( |
| output_blob_name, |
| )), |
| ) |
| if test_shape_inference: |
| self._assertInferTensorChecks( |
| output_blob_name, shapes, types, output, |
| ensure_output_is_inferred=ensure_outputs_are_inferred) |
| outs.append(output) |
| if grad_reference is not None: |
| assert output_to_grad is not None, \ |
| "If grad_reference is set," \ |
| "output_to_grad has to be set as well" |
| |
| with core.DeviceScope(device_option): |
| self._assertGradReferenceChecks( |
| op, inputs, reference_outputs, |
| output_to_grad, grad_reference, |
| threshold=threshold) |
| |
| return outs |
| |
| def assertValidationChecks( |
| self, |
| device_option, |
| op, |
| inputs, |
| validator, |
| input_device_options=None, |
| as_kwargs=True, |
| init_net=None, |
| ): |
| if as_kwargs: |
| assert len(set(list(op.input) + list(op.output))) == \ |
| len(op.input) + len(op.output), \ |
| "in-place ops are not supported in as_kwargs mode" |
| op = copy.deepcopy(op) |
| op.device_option.CopyFrom(device_option) |
| |
| with temp_workspace(): |
| _input_device_options = input_device_options or \ |
| core.InferOpBlobDevicesAsDict(op)[0] |
| for (n, b) in zip(op.input, inputs): |
| workspace.FeedBlob( |
| n, |
| b, |
| device_option=_input_device_options.get(n, device_option) |
| ) |
| if init_net: |
| workspace.RunNetOnce(init_net) |
| workspace.RunOperatorOnce(op) |
| outputs = [workspace.FetchBlob(n) for n in op.output] |
| if as_kwargs: |
| validator(**dict(zip( |
| list(op.input) + list(op.output), inputs + outputs))) |
| else: |
| validator(inputs=inputs, outputs=outputs) |
| |
| def assertRunOpRaises( |
| self, |
| device_option, |
| op, |
| inputs, |
| input_device_options=None, |
| exception=(Exception,), |
| regexp=None, |
| ): |
| op = copy.deepcopy(op) |
| op.device_option.CopyFrom(device_option) |
| |
| with temp_workspace(): |
| _input_device_options = input_device_options or \ |
| core.InferOpBlobDevicesAsDict(op)[0] |
| for (n, b) in zip(op.input, inputs): |
| workspace.FeedBlob( |
| n, |
| b, |
| device_option=_input_device_options.get(n, device_option) |
| ) |
| if regexp is None: |
| self.assertRaises(exception, workspace.RunOperatorOnce, op) |
| else: |
| six.assertRaisesRegex( |
| self, exception, regexp, workspace.RunOperatorOnce, op) |
